Back

Pre-Registered Interim Analysis Designs (PRIADs): Increasing the Cost-Effectiveness of Hypothesis Testing.

  • Published In: Journal of Consumer Research, 2024, v. 51, n. 4. P. 845 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: André, Quentin; Reinholtz, Nicholas 3 of 3

Abstract

The difficulty of determining how many observations to collect is a source of inefficiency in consumer behavior research. Group sequential designs, which allow researchers to perform interim analyses while data collection is ongoing, could offer a remedy. However, they are scarcely used in consumer behavior research, probably owing to low awareness, perceived complexity, or concerns about the validity of this approach. This article offers a tutorial on group sequential designs and introduces Pre-Registered Interim Analysis Designs (PRIADs): A practical five-step procedure to facilitate the adoption of these designs in marketing. We show that group sequential designs can be easily adopted by marketing researchers, and introduce a companion app to help researchers implement them. We demonstrate multiple benefits of PRIADs for researchers engaged in confirmatory hypothesis testing: They facilitate sample size decisions, allow researchers to achieve a desired level of statistical power with a smaller number of observations, and help conduct more efficient pilot studies. We validate this cost-saving potential through a comprehensive re-analysis of 212 studies published in the Journal of Consumer Research , which shows that using PRIADs would have reduced participant costs by 20–29%. We conclude with a discussion of limitations and possible alternatives to PRIADs. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Consumer Research. 2024/12, Vol. 51, Issue 4, p845
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0093-5301
  • DOI:10.1093/jcr/ucae028
  • Accession Number:180973389
  • Copyright Statement:Copyright of Journal of Consumer Research is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.